Assessing GPU Porting APIs: CUDA, OpenCL and SYCL Compatibility

Choosing between CUDA, OpenCL and SYCL for a port comes down to one rule. Test the target accelerator’s whole stack against the operations and kernels your pipeline actually uses, and do not assume compatibility from the API name or the spec sheet. That stack includes the framework build, the driver version, the OpenCL extensions and optional features, the vendor’s conformance record and any third-party plugins. An API decides how much of your code moves across. Only the target’s stack decides whether that code runs correctly and fast enough.

TechnoLynx does this assessment as part of its GPU Performance Engineering offer. It starts by listing the exact ops, kernels, extensions and plugins the pipeline depends on. It then tests the target hardware and software against that list directly, ports or writes whatever is missing in CUDA, OpenCL, SYCL or HIP, and documents the result as an auditable compatibility record for procurement or sign-off.

CUDA, OpenCL and SYCL as porting targets

The three APIs reach different hardware, and each gives a different route out of an existing CUDA codebase. CUDA is NVIDIA’s own programming model and runs only on NVIDIA GPUs. OpenCL is an open, royalty-free Khronos standard for CPUs, GPUs and other processors. SYCL is a single-source C++ model for heterogeneous computing, also from Khronos, and SYCL 2020 is the latest specification.

	CUDA	OpenCL	SYCL
What it is	Proprietary NVIDIA language extension	Open standard: API, kernel language and intermediate language	Open standard C++ language extension
GPU vendors reached	NVIDIA	NVIDIA, AMD, Intel, Qualcomm Adreno, Arm Mali and Imagination PowerVR, among listed conformant products	NVIDIA, AMD, Intel
Route from existing CUDA	None needed	Manual kernel conversion: minimal edits with NVIDIA’s tools in one measured study, more with ATI’s	SYCLomatic migrates 90-95% of CUDA code from CUDA 8.0 to 12.9 and flags the rest with warnings
First check on the target	Driver meets the toolkit’s minimum	Version, optional features and extensions, queried per device	Compiler support, plus the plugin for AMD or NVIDIA targets

The migration tools are useful, but they do not finish the job. In SYCLomatic’s own workflow, manual review, rebuilding and a validation stage come after the automated conversion. AMD says its HIPIFY tools convert almost all CUDA runtime code. It also says extra porting may be needed for architecture feature queries or for CUDA capabilities that HIP does not support. When HIP is the realistic fallback for an AMD target, our page on porting an inference path off CUDA with HIP goes into that route in detail.

Is there a credible alternative to CUDA, and what is lost when part of the pipeline is CUDA-only?

Yes, but code that ports cleanly is not the same as code that performs well, so the alternatives have to be measured on your kernels. The authors of one measured CUDA versus OpenCL study, using near-identical Quantum Monte Carlo kernels, note that OpenCL’s generality may carry a performance penalty. Qualcomm’s Adreno OpenCL guide puts it more bluntly: OpenCL programs are portable, but OpenCL performance is not, because implementations depend on each vendor’s architecture.

The multi-model study by Davis et al. is more encouraging for SYCL. On NVIDIA systems, CUDA was the best model or within 3% of it in eight of ten cases. SYCL was very often competitive with the native CUDA and HIP ports, sometimes faster, and was never the worst port except in XSBench on Perlmutter. A 2023 CUDA versus SYCL comparison on Smith-Waterman protein search found similar performance on NVIDIA and good code portability to AMD and Intel GPUs. Results can still turn badly. In a 2026 port of the gPLUTO astrophysics code, the same OpenMP code ran about three times slower on AMD MI250X than the OpenACC baseline on NVIDIA A100, with some kernels up to an order of magnitude slower. That is why cross-platform performance portability is its own engineering problem, and why a general CUDA vs OpenCL performance comparison cannot replace a run of your own kernels.

What you lose with a CUDA-only component depends on what that component is. AMD lists features that PyTorch on ROCm does not currently support: TF32 matmul control, NVTX profiling integration, lazy NVRTC loading and torch-tensorrt, for which ROCm has no equivalent library. In graphics tools the loss can be a feature rather than an error. Chaos says Enscape supports both AMD and NVIDIA GPUs, but hardware ray tracing is not available on most AMD cards, and the full feature set is only available on NVIDIA RTX cards.

CUDA-dependent CAD, BIM and render plugins

Where a plugin maker has published nothing about ROCm or OpenCL, treat the plugin as unverified rather than unsupported, and settle it with a direct question to the vendor or a trial on loan hardware. If one tool in the workflow is locked to CUDA, a mixed fleet can work: NVIDIA machines for the CUDA-bound visualisation seats, other hardware for the rest. The cost is a second driver stack and validation path to maintain.

How do we verify that the target toolchain supports every operation and kernel we use?

Start with an inventory, not with the device. List every kernel, library call, OpenCL extension, data type and framework op the pipeline touches, then check each item against the target. On OpenCL, version 3.0 makes this check more important. OpenCL 3.0 made everything beyond OpenCL 1.2 an optional feature that can be queried. OpenCL 1.2 applications run unchanged, but OpenCL 2.x applications work only if the driver supports every feature they use. Qualcomm’s guide gives the reason: making these features optional lowered the bar for conformance, so embedded devices and FPGAs can be certified without high-complexity features such as shared virtual memory.

Diagnostic checklist for an OpenCL target

Read the platform and device version strings, and the numeric version queries CL_PLATFORM_NUMERIC_VERSION and CL_DEVICE_NUMERIC_VERSION.
Query CL_DEVICE_EXTENSIONS_WITH_VERSION (or the CL_DEVICE_EXTENSIONS string through clGetDeviceInfo) and check every extension your kernels use.
Query the device’s list of optional OpenCL C features, which the compiler reports together with the OpenCL C version that introduced each feature macro.
Check the 3.0 capability queries: CL_DEVICE_ATOMIC_MEMORY_CAPABILITIES, CL_DEVICE_ATOMIC_FENCE_CAPABILITIES, CL_DEVICE_NON_UNIFORM_WORK_GROUP_SUPPORT and CL_DEVICE_WORK_GROUP_COLLECTIVE_FUNCTIONS_SUPPORT.
For image pipelines, confirm CL_DEVICE_IMAGE_SUPPORT and the formats returned by clGetSupportedImageFormats, which returns an empty list if no device supports images.
Check image dimension limits such as CL_DEVICE_IMAGE2D_MAX_WIDTH and CL_DEVICE_IMAGE2D_MAX_HEIGHT against your largest frames.
Read the device’s report of the latest conformance test suite version it has fully passed.
Compile and run the real kernels on the target driver, because none of the queries above proves numerical correctness.

Which extensions an image or convolution pipeline should check first

Sub-groups, read-write images, creating a 2D image from a buffer, and the sRGB and depth channel orders are all optional on OpenCL 3.0 devices.

Vendor extensions need extra care. In Qualcomm’s IWOCL 2022 overview, KHR extensions require adoption by several vendors and passing conformance tests. EXT and vendor extensions require neither, and Qualcomm recommends KHR extensions where they offer similar functionality. Adreno’s cl_qcom_ml_ops covers convolution, pooling, GEMM and softmax, and cl_qcom_other_image reads YUV formats such as NV12. Both tie the port to one vendor. Qualcomm also warns that extensions may not carry forward to new devices, and that features such as image limits and formats vary across Adreno families even within one OpenCL version.

Does an SoC’s OpenCL driver passing Khronos conformance mean we can port to it safely?

No. Conformance is a check you need, but passing it does not prove your kernels will run correctly. Khronos runs conformance testing to give an objective, multi-vendor definition of conformance. A product counts as conformant when it uses materially equivalent device ISA and binaries, or updates that cause no previously passing test to fail, and the same major and minor OpenCL version with the same extensions or a subset. The conformant-product list therefore names specific driver builds, for example Qualcomm’s Adreno 840 under Android 16 with a stated compiler build. A driver update stays covered only if it causes no previously passing test to fail, so check which build your target actually runs.

Implementations also ship with compiler bugs. In PLDI 2015 fuzzing work, Lidbury et al. tested 19 OpenCL device-and-driver configurations from several vendors and found a significant number of possible compiler defects in every configuration. If the vendor publishes no results, you can build and run the open-source suite yourself. The OpenCL-CTS repository covers all versions of the standard and builds against a vendor OpenCL library on Linux, Windows, macOS and Android. A missing conformance result does not show that a device lacks support. It shows that support cannot be verified until someone tests it.

Which CUDA, driver and framework versions to pin before committing

On NVIDIA targets the question is a version chain, and each link has its own rule. NVIDIA’s data centre driver documentation says the driver API is backwards compatible, so newer drivers run applications built with older toolkits, but each toolkit needs a minimum driver. From CUDA 11 onwards, minor version compatibility lets an application built in one major family run, with a limited feature set, on the family’s minimum driver: 525 or newer for CUDA 12.x and 580 or newer for CUDA 13.x. There are two exceptions. Applications that compile device code to PTX do not work on older drivers, and features that span both toolkit and driver can fail with cudaErrorCallRequiresNewerDriver.

PyTorch adds a further link. Each PyTorch release is compiled against specific CUDA versions, for example PyTorch 2.4 against CUDA 12.4, 12.1 or 11.8, and the driver must support the CUDA version the wheel was built with. The GIGAGPU compatibility matrix notes that a default pip install torch without the right index URL can install a CPU-only build, which leaves the GPU invisible even when the driver and hardware are fine. Architecture support has an end date too: NVIDIA’s matrix lists CUDA 12.x as the last toolkit and R580 as the last driver for Volta.

Which PyTorch build should we confirm for a non-NVIDIA GPU?

On AMD, the builds to check are the ROCm build of upstream PyTorch and AMD’s validated PyTorch Docker images with ROCm backends. ROCm support is upstreamed into the official PyTorch repository, and PyTorch’s HIPification generates HIP source from the CUDA backend automatically. torch.compile uses OpenAI Triton as a fundamental component on NVIDIA and AMD GPUs. In an AMD ROCm blog, torch.compile improved ResNet-152 by more than 3.5 times and ViT by more than 2.3 times on an MI210, with ROCm 6.0+ and PyTorch 2.0+ as the stated environment. For mixed precision, torch.amp lets you check whether autocast is available for each device type, and the torch.cuda.amp.autocast form is deprecated in favour of torch.amp.autocast("cuda").

Snapdragon targets need a different check, because what matters is the op set of the execution provider, not CUDA compatibility. ONNX Runtime’s QNN Execution Provider does not support dynamic shapes, supports only a subset of ONNX operators (Loops and Ifs, for instance, are not supported), and its HTP backend runs quantised models only. Its documentation names the QNN version and Qualcomm SoCs it was built and tested with.

If a required op or extension is missing, what are the fallbacks?

Each fallback in the table below fits a different kind of gap. Deformable attention is one example. Researchers working on Ascend NPUs report that multi-scale deformable attention takes over 50% of end-to-end inference latency in Deformable DETR, and that deploying it efficiently on domain-specific accelerators remains hard.

Fallback	When it fits	What it looks like
Custom kernel or plugin	The op is critical and the target has a programmable path	The BEVFormer TensorRT project implements grid sampler, multi-scale deformable attention and modulated deformable conv as custom plugins in FP32, FP16 and INT8
Restructure the model	The op is replaceable without unacceptable accuracy loss	WidthFormer reports 1.5 ms latency on an NVIDIA 3090 and 2.8 ms on Horizon Journey-5 at 256×704 input
Different API	The silicon is fine but the API path is immature	Move a CUDA kernel to HIP or SYCL through HIPIFY or SYCLomatic, then review it by hand
Different silicon	The gap is in hardware capability, or the vendor stack keeps failing tests	Re-run the inventory against the next candidate

Support durability: driver branches, freezes and attestation

Whether an op works today and whether the stack stays supported are separate questions. Regulated and long-lived deployments need answers to both. NVIDIA designates R535 and R580 as long-term support branches, with end-of-life in June 2026 and June 2028 respectively, while R610 is a new feature branch ending in August 2026. A multi-year validated image needs a written commitment covering the exact driver and firmware versions, plus a stated plan for the case where a mandatory security patch breaks the freeze. Ask the provider in writing, because the documentation behind this article does not settle what any provider will commit to.

Can we commit a migration date now, or do we need a feasibility spike first?

Run the spike first. Before a migration date goes into the programme plan, port a small, representative set of kernels to the target SoC’s real OpenCL driver and measure them against the real-time budget. A vendor spec does not tell you whether SLAM or obstacle detection will hold frame rate after the port.

TechnoLynx runs this kind of migration feasibility assessment. It picks the minimal kernel set with the team, ports those kernels to SYCL or Vulkan compute on the target SoC within a fixed time box, measures latency and throughput against the programme’s deadlines on the actual ICD, and reports a go/no-go decision with the risk areas named. If the result is positive, it can scope the full port.

The kernel budget shows why a small sample is informative. In the original KinectFusion pipeline, one frame runs between 18 and 54 GPU kernels, so 30 frames per second means sustaining 540 to 1,620 kernel executions every second. That is 18 × 30 and 54 × 30.

Spike procedure

Confirm the driver loads through the ICD loader. The loader checks that cl_khr_icd appears in the platform extension string and queries the vendor suffix, and it ignores a vendor ICD if any step fails.
Pick the most compute-heavy SLAM front-end kernel, one obstacle-detection kernel and one memory-bound kernel with an awkward access pattern.
Port them to SYCL or Vulkan compute, and treat the two as separate risk profiles if both are viable.
Time each kernel with queue profiling (CL_QUEUE_PROFILING_ENABLE and clGetEventProfilingInfo) and time the pipeline end to end with a CPU timer, since Qualcomm notes the GPU timer misses work such as cache flushes and host synchronisation.
Commit the date only if the kernels meet the latency target and the ICD shows no blocking gaps. Otherwise revise the scope or the timeline first.

The spike has limits. Kernels that pass may not represent the rest of the kernel set. Driver maturity can also change under you: Qualcomm says a binary built with one version of its OpenCL software is not guaranteed to work with a newer version on the same device, and recommends building from source as the fallback. Record the firmware and driver build next to every spike result. A firmware update can quietly invalidate the go decision you based on those numbers.